{"id":"https://openalex.org/W4327520748","doi":"https://doi.org/10.1145/3576938.3576951","title":"Comparison of Feature Selection Methods for Bottom-up of 1p/19q Co-deletion Prediction Performance in Grade 2 and 3 Gliomas","display_name":"Comparison of Feature Selection Methods for Bottom-up of 1p/19q Co-deletion Prediction Performance in Grade 2 and 3 Gliomas","publication_year":2022,"publication_date":"2022-11-10","ids":{"openalex":"https://openalex.org/W4327520748","doi":"https://doi.org/10.1145/3576938.3576951"},"language":"en","primary_location":{"id":"doi:10.1145/3576938.3576951","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3576938.3576951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Digital Medicine and Image Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002827804","display_name":"Tatsuaki Kobayashi","orcid":"https://orcid.org/0000-0003-1722-2403"},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tatsuaki Kobayashi","raw_affiliation_strings":["Graduate School of Environment and Information Sciences, Yokohama National University, Japan"],"raw_orcid":"https://orcid.org/0000-0003-1722-2403","affiliations":[{"raw_affiliation_string":"Graduate School of Environment and Information Sciences, Yokohama National University, Japan","institution_ids":["https://openalex.org/I180203408"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077771833","display_name":"Tomoharu Nagao","orcid":"https://orcid.org/0000-0002-2841-9538"},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoharu Nagao","raw_affiliation_strings":["Graduate School of Environment and Information Sciences, Yokohama National University, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2841-9538","affiliations":[{"raw_affiliation_string":"Graduate School of Environment and Information Sciences, Yokohama National University, Japan","institution_ids":["https://openalex.org/I180203408"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002827804"],"corresponding_institution_ids":["https://openalex.org/I180203408"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33438927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"77","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10129","display_name":"Glioma Diagnosis and Treatment","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10129","display_name":"Glioma Diagnosis and Treatment","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11287","display_name":"Cancer Genomics and Diagnostics","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.773310661315918},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7731359004974365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6965053081512451},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6489729285240173},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6273690462112427},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6062323451042175},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5464692115783691},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5281310677528381},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4599546194076538},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45047932863235474},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4474925994873047},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4241853356361389},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42277202010154724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27354806661605835}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.773310661315918},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7731359004974365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6965053081512451},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6489729285240173},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6273690462112427},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6062323451042175},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5464692115783691},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5281310677528381},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4599546194076538},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45047932863235474},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4474925994873047},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4241853356361389},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42277202010154724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27354806661605835},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3576938.3576951","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3576938.3576951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Digital Medicine and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2047296435","https://openalex.org/W2069303197","https://openalex.org/W2128739912","https://openalex.org/W2545260123","https://openalex.org/W2770261599","https://openalex.org/W2775589368","https://openalex.org/W2808774136","https://openalex.org/W2897540743","https://openalex.org/W2962316502","https://openalex.org/W2997384822","https://openalex.org/W3033217117","https://openalex.org/W3042383278","https://openalex.org/W3053264094","https://openalex.org/W3148962947","https://openalex.org/W3174246647","https://openalex.org/W3182597399","https://openalex.org/W3214016363","https://openalex.org/W4283830577"],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W183202219","https://openalex.org/W3095877357","https://openalex.org/W2072565696","https://openalex.org/W2050451745","https://openalex.org/W4386121542"],"abstract_inverted_index":{"This":[0],"study":[1],"aimed":[2],"to":[3,30,154],"compare":[4],"multiple":[5],"feature":[6,33,113,118,145],"selection":[7,34,114,119,146],"methods":[8],"that":[9],"may":[10,179],"bottom-up":[11],"1p/19q":[12,41,156,176],"co-deletion":[13,42,157,177],"prediction":[14,83,178,181],"performances":[15,84],"for":[16,111,175],"grade":[17,159],"2":[18,160],"and":[19,26,58,126,151,161,187],"3":[20,162],"gliomas":[21],"using":[22,68,96,138],"random":[23,51],"initialization,":[24],"imagenet,":[25],"transfer-learned":[27,59],"deep":[28,53,56,60,171],"features":[29,61,78,99,141,165,186],"identify":[31],"effective":[32],"methods.":[35,115],"The":[36],"Cancer":[37],"Imaging":[38],"Data":[39],"Archive":[40],"dataset":[43],"was":[44],"used":[45],"in":[46,158],"this":[47],"study.":[48],"Image-based":[49],"features,":[50,54,57],"initialization":[52],"imagenet":[55],"were":[62,91],"extracted":[63,166],"from":[64,167],"T1-weighted":[65],"post-contrast":[66],"images":[67],"the":[69,82,101,105,121,134,148,168],"tumor":[70],"region":[71],"of":[72,85,88,124,130],"interest":[73],"mask":[74],"images.":[75],"Using":[76],"these":[77],"as":[79],"learning":[80,170],"data,":[81],"seven":[86],"types":[87],"predictive":[89,135],"models":[90,94],"compared.":[92],"Predictive":[93],"created":[95,137,174],"only":[97,139],"image-based":[98,140,185],"had":[100],"highest":[102,122,149],"area":[103],"under":[104],"receiver":[106],"operating":[107],"characteristic":[108],"curve":[109],"(AUC)":[110],"all":[112],"Genetic":[116],"algorithm":[117,144],"yielded":[120,147],"AUC":[123,150],"0.69":[125],"a":[127],"minimal":[128,152],"log-loss":[129],"0.65.":[131],"In":[132],"conclusion,":[133],"model":[136],"with":[142],"genetic":[143],"log-loss,":[153],"predict":[155],"gliomas.":[163],"Deep":[164],"post-transfer":[169],"neural":[172],"network":[173],"improve":[180],"performance":[182],"by":[183],"combining":[184],"clinical":[188],"variables.":[189]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}